internal static extern void cveSVMSetType(
IntPtr obj,
SVM.SvmType val);
/// <summary>
/// Trains SVM using Bio inspired features.
/// </summary>
/// <param name="trainData"> Feature vectors for training. </param>
/// <param name="trainClasses"> Class vector of instances for training. </param>
/// <param name="saveModelName"> saved Model name using </param>
/// <param name="svm_type"> Svm type. </param>
/// <param name="C"> Cost. </param>
/// <param name="coef0"> Coeff. </param>
/// <param name="degree"> Degree. </param>
/// <param name="eps"> Eps for termination criteria. </param>
/// <param name="gamma"> Gamma. </param>
/// <param name="kernel_type"> Kernel Type of svm. </param>
/// <param name="nu"> Nu. </param>
/// <param name="maxIter"> Maximun number of iterations. </param>
/// <param name="termCritType"> Termiantion Criteria Type. </param>
/// <returns></returns>
public bool TrainSVM(Matrix <float> trainData, Matrix <int> trainClasses, string saveModelName, SVM.SvmType svm_type = SVM.SvmType.CSvc, int kFold = 10, double C = 1.0, double coef0 = 0.1, int degree = 3, double eps = 0.001, double gamma = 1.0, SVM.SvmKernelType kernel_type = SVM.SvmKernelType.Rbf, double nu = 0.5, int maxIter = 500, Emgu.CV.CvEnum.TermCritType termCritType = Emgu.CV.CvEnum.TermCritType.Eps)
{
var svmModel = new SVM();
var termCriteria = new Emgu.CV.Structure.MCvTermCriteria();
var trainSampleCount = trainData.Rows;
svmModel.C = C;
svmModel.Coef0 = coef0;
svmModel.Degree = degree;
svmModel.Gamma = gamma;
svmModel.Nu = nu;
svmModel.Type = svm_type;
termCriteria.Epsilon = eps;
termCriteria.MaxIter = maxIter;
termCriteria.Type = termCritType;
svmModel.TermCriteria = termCriteria;
svmModel.P = 1;
svmModel.SetKernel(kernel_type);
bool trained;
try
{
using (svmModel)
{
TrainData td = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses);
trained = svmModel.TrainAuto(td, kFold);
svmModel.Save(saveModelName);
}
}
catch (Exception esvm)
{
throw esvm;
}
return(trained);
}
